Senior Data Scientist

Role: Senior Data Scientist

Location: London, UK (Hybrid)

Employment type: Contract

Accountabilities

• The Data Scientist has full-stack accountabilities across the full value chain of building an industrialized data-science software product:
• Understanding a business problem and its component processes end to end, and identifying opportunities to make decisions more optimally leveraging decision-support tooling
• Efficiently conducting analyses and visualizations to identify valuable opportunities for decision-support and to determine trade-offs between different potential feature implementations
• Prototyping advanced machine learning and optimization models to prove the value of a use case and approach (in Python)
• Delivering features to industrialize machine learning and optimization models in Python using best-practice software principles (e.g., strict typing, classes, testing)
• Build automated, robust data cleaning pipelines that follow software best-practices (in Python)
• Implementing integrations between the core algorithm (machine-learning or optimization) and a workflow orchestration paradigm such as Dagster
• Implementing software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles
• Building logging, error handling, and automated tests (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products
• Deliver features to harden an algorithm against edge cases in the operation and in data
• Conduct analysis to quantify the adoption and value-capture from a decision-support product
• Engage with business stakeholders to collect requirements and get feedback
• Contribute to conversations on feature prioritisation and roadmap, with an understanding of the trade-off between speed vs. long-term value
• Understand and integrate the product into existing business processes, and contribute to the development and adoption of new business processes leveraging a decision-support product
• Communicate feature and modeling approach, trade-offs, and results with the internal team and business stakeholders
• The Data Scientist is also accountable for ways of working fit for an Agile cross-functional development squad, including:
• Using Git-versioning best practices for version control
• Contributing and reviewing pull-requests and product / technical documentation
• Giving input on prioritization, team process improvements, optimizing technology choices
• Working independently and giving predictability on delivery timelines

Skills/capabilities

• Strong knowledge of either machine learning and optimization techniques, incl. supervised (regression, tree methods, etc.), unsupervised (clustering) learning, and operations research (linear, mixed integer programming, heuristics)
• Fluent in Python(required) and other programming languages (preferred)with strong skills in applying DS, ML, and OR packages (scikit-learn, pandas, numpy, gurobietc.) to solve real-life problems and visualise the outcomes (e.g. seaborn)
• Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow)
• Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker, ECS) nice to have
• Experience in code testing (unit, integration, end-to-end tests)
• Strong data engineering skills in SQL and Python
• Proficient in use of Microsoft Office, including advanced Excel and PowerPoint Skills
• Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights
• Understanding of the trade-offs of different data science, machine learning, and optimization approaches, and ability to intelligently select which are the best candidates to solve a particular business problem
• Able to structure business and technical problems, identify trade-offs, and propose solutions
• Communication of advanced technical concepts to audiences with varying levels of technical skills
• Managing priorities and timelines to deliver features in a timely manner that meet business requirements
• Collaborative team-working, giving and receiving feedback, and always seeking to improve team processes

Qualifications/experience

• Master’s degree or greater in data science, ML, or operational research, or 2+ years of highly relevant industry experience(required)
• 0-2 years working on production ML or optimization software products at scale (required)
• Experience in developing industrialized software, especially data science or machine learning software products (preferred)
• Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred)